# Setup Airflow
Setup a production-level Airlow cluster using Docker-compose.

 (source internet)

# Code struct

├── app
│   ├── .env
│   ├── dev.env
│   ├── docker-compose-flower.yaml
│   ├── docker-compose-init.yaml
│   ├── docker-compose-worker.yaml
│   └── docker-compose.yaml
└── build
    ├── Dockerfile
    ├── README.md
    └── libs
        ├── instantclient-basic-linux.x64-21.16.0.0.0dbru.zip
        └── requirements-local.txt        

# Build Airflow custom image
Extend an Airflow image for working with Dbt, Redshit, Oracle DB.

* requirements-local.txt

airflow-dbt==0.4.0
dbt-redshift==1.7.0
dbt-core==1.7.1
oracledb==2.5.0
apache-airflow-providers-common-sql
apache-airflow-providers-oracle[common.sql]
flower==2.0.1
gunicorn==20.1.0
itsdangerous==2.0.1
Flask-OpenID==1.3.0
flask-oidc==1.4.0
astronomer-cosmos==1.6.0

* Dockerfile:

FROM apache/airflow:2.9.3

USER root

# Install Oracle binary
# https://www.oracle.com/vn/database/technologies/instant-client/linux-x86-64-downloads.html

RUN apt update && apt install unzip libaio1
RUN mkdir -p /opt/oracle && chown -R airflow: /opt/oracle

COPY ./libs/instantclient-basic-linux.x64-21.16.0.0.0dbru.zip /opt/oracle/
RUN cd /opt/oracle && unzip instantclient-basic-linux.x64-21.16.0.0.0dbru.zip
RUN echo /opt/oracle/instantclient_21_16 > /etc/ld.so.conf.d/oracle-instantclient.conf
RUN ldconfig

# Install Python requirements
COPY --chown=airflow:root ./libs/requirements-local.txt ./requirements/

USER airflow
RUN pip install -r requirements/requirements-local.txt

* Build image:

docker build -t my-com/airflow:2.9.3 -f Dockerfile .

Wait for build to success and check images by `docker images list | grep airflow`

# Setup databases
## Metadata database
Run a PostgreSQL on your host machine:

docker run -d --name postgres -p 5432:5432 -e POSTGRES_USER=postgres -e POSTGRES_PASSWORD=t0b3changed \
--restart unless-stopped \
-e PGDATA=/var/lib/postgresql/data/pgdata -v /path/to/postgres/data/:/var/lib/postgresql/data -d postgres:15

`/path/to/postgres/data/` path to a folder on your host for Postgres to persist data.

* Create database and user for Airflow:

create database airflow;
create user airflow with password 'my_airflow_pwd';

# Select/switch to airflow db
\c airflow

GRANT ALL ON SCHEMA public TO airflow;
GRANT ALL PRIVILEGES ON ALL TABLES IN SCHEMA public TO airflow;
GRANT ALL PRIVILEGES ON ALL SEQUENCES IN SCHEMA public TO airflow;

## Task queue
Run a Redis on your host machine:

docker run -d --name redis -p 6379:6379 redis:7.4.1 --save 60 1 --loglevel warning

`--save 60 1` allow Redis to persist data every 60s

# Deployment configs
## Environment
Update env vars (.env) for deployment

# User CeleryExecutor
AIRFLOW__CORE__EXECUTOR='CeleryExecutor'

# Connection to metadata server (PostgreSQL), queue (Redis)
AIRFLOW__DATABASE__SQL_ALCHEMY_CONN='postgresql+psycopg2://airflow:airflow_pwd@host_name/airflow'
AIRFLOW__CELERY__RESULT_BACKEND='db+postgresql://airflow:airflow_pwd@host_name/airflow'
AIRFLOW__CELERY__BROKER_URL='redis://host_name:6379/1'

AIRFLOW__CORE__FERNET_KEY='_SWDqtSH05c-yrITFolOXMPDmSJE-aRU6LcEb1ffMwo='

AIRFLOW__CORE__DEFAULT_TIMEZONE='Asia/Ho_Chi_Minh'

AIRFLOW__CORE__DAGS_ARE_PAUSED_AT_CREATION='true'
AIRFLOW__CORE__LOAD_EXAMPLES='false'
AIRFLOW__API__AUTH_BACKENDS='airflow.api.auth.backend.basic_auth,airflow.api.auth.backend.session'

AIRFLOW__SCHEDULER__ENABLE_HEALTH_CHECK='true'
# AIRFLOW_CONFIG='/opt/airflow/config/airflow.cfg'

## Docker-compose configs
* docker-compose.yaml:
Base components of Airflow cluster (webserver, scheduler, trigger)

x-airflow-common:
  &airflow-common
  image: my-com/airflow:2.9.3
  env_file: .env
  volumes:
    - /path/to/airflow/data/dags:/opt/airflow/dags
    - /path/to/airflow/data/logs:/opt/airflow/logs
    - /path/to/airflow/data/config:/opt/airflow/config
    - /path/to/airflow/data/plugins:/opt/airflow/plugins
  user: "${AIRFLOW_UID:-50000}:0"

services:
  airflow-webserver:
    <<: *airflow-common
    command: webserver
    ports:
      - "8080:8080"
    healthcheck:
      test: ["CMD", "curl", "--fail", "http://localhost:8080/health"]
      interval: 30s
      timeout: 10s
      retries: 5
      start_period: 30s
    restart: always

  airflow-scheduler:
    <<: *airflow-common
    command: scheduler
    healthcheck:
      test: ["CMD", "curl", "--fail", "http://localhost:8974/health"]
      interval: 30s
      timeout: 10s
      retries: 5
      start_period: 30s
    restart: always

  airflow-triggerer:
    <<: *airflow-common
    command: triggerer
    healthcheck:
      test: ["CMD-SHELL", 'airflow jobs check --job-type TriggererJob --hostname "$${HOSTNAME}"']
      interval: 30s
      timeout: 10s
      retries: 5
      start_period: 30s
    restart: always

* docker-compose-worker.yaml:
Deploy workers node (replicas 3)

x-airflow-common:
  &airflow-common
  image: my-com/airflow:2.9.3
  env_file: .env
  volumes:
    &volumes
    - /path/to/airflow/data/dags:/opt/airflow/dags
    - /path/to/airflow/data/logs:/opt/airflow/logs
    - /path/to/airflow/data/config:/opt/airflow/config
    - /path/to/airflow/data/plugins:/opt/airflow/plugins
  user: "${AIRFLOW_UID:-50000}:0"

services:
  airflow-worker:
    <<: *airflow-common
    command: celery worker
    healthcheck:
      # yamllint disable rule:line-length
      test:
        - "CMD-SHELL"
        - 'celery --app airflow.providers.celery.executors.celery_executor.app inspect ping -d "celery@$${HOSTNAME}" || celery --app airflow.executors.celery_executor.app inspect ping -d "celery@$${HOSTNAME}"'
      interval: 30s
      timeout: 10s
      retries: 5
      start_period: 30s  
    environment:
      # Required to handle warm shutdown of the celery workers properly
      # See https://airflow.apache.org/docs/docker-stack/entrypoint.html#signal-propagation
      DUMB_INIT_SETSID: "0"
    restart: always
    deploy:
      mode: replicated
      replicas: 3

  

* docker-compose-init.yaml:
Init Airflow data, configs

x-airflow-common:
  &airflow-common
  image: my-com/airflow:2.9.3
  env_file: .env
  volumes:
    &volumes
    - /path/to/airflow/data/dags:/opt/airflow/dags
    - /path/to/airflow/data/logs:/opt/airflow/logs
    - /path/to/airflow/data/config:/opt/airflow/config
    - /path/to/airflow/data/plugins:/opt/airflow/plugins
  user: "${AIRFLOW_UID:-50000}:0"

services:
  airflow-init:
    <<: *airflow-common
    entrypoint: /bin/bash
    # yamllint disable rule:line-length
    command:
      - -c
      - |
        if [[ -z "${AIRFLOW_UID}" ]]; then
          echo
          echo -e "\033[1;33mWARNING!!!: AIRFLOW_UID not set!\e[0m"
          echo "If you are on Linux, you SHOULD follow the instructions below to set "
          echo "AIRFLOW_UID environment variable, otherwise files will be owned by root."
          echo "For other operating systems you can get rid of the warning with manually created .env file:"
          echo "    See: https://airflow.apache.org/docs/apache-airflow/stable/howto/docker-compose/index.html#setting-the-right-airflow-user"
          echo
        fi
        one_meg=1048576
        mem_available=$$(($$(getconf _PHYS_PAGES) * $$(getconf PAGE_SIZE) / one_meg))
        cpus_available=$$(grep -cE 'cpu[0-9]+' /proc/stat)
        disk_available=$$(df / | tail -1 | awk '{print $$4}')
        warning_resources="false"
        if (( mem_available < 4000 )) ; then
          echo
          echo -e "\033[1;33mWARNING!!!: Not enough memory available for Docker.\e[0m"
          echo "At least 4GB of memory required. You have $$(numfmt --to iec $$((mem_available * one_meg)))"
          echo
          warning_resources="true"
        fi
        if (( cpus_available < 2 )); then
          echo
          echo -e "\033[1;33mWARNING!!!: Not enough CPUS available for Docker.\e[0m"
          echo "At least 2 CPUs recommended. You have $${cpus_available}"
          echo
          warning_resources="true"
        fi
        if (( disk_available < one_meg * 10 )); then
          echo
          echo -e "\033[1;33mWARNING!!!: Not enough Disk space available for Docker.\e[0m"
          echo "At least 10 GBs recommended. You have $$(numfmt --to iec $$((disk_available * 1024 )))"
          echo
          warning_resources="true"
        fi
        if [[ $${warning_resources} == "true" ]]; then
          echo
          echo -e "\033[1;33mWARNING!!!: You have not enough resources to run Airflow (see above)!\e[0m"
          echo "Please follow the instructions to increase amount of resources available:"
          echo "   https://airflow.apache.org/docs/apache-airflow/stable/howto/docker-compose/index.html#before-you-begin"
          echo
        fi
        mkdir -p /sources/logs /sources/dags /sources/plugins
        chown -R "${AIRFLOW_UID}:0" /sources/{logs,dags,plugins}
        exec /entrypoint airflow version
    # yamllint enable rule:line-length
    environment:
      _AIRFLOW_DB_MIGRATE: 'true'
      _AIRFLOW_WWW_USER_CREATE: 'true'
      _AIRFLOW_WWW_USER_USERNAME: ${_AIRFLOW_WWW_USER_USERNAME:-airflow}
      _AIRFLOW_WWW_USER_PASSWORD: ${_AIRFLOW_WWW_USER_PASSWORD:-airflow}
      _PIP_ADDITIONAL_REQUIREMENTS: ''
    user: "0:0"
    volumes:
      - ${AIRFLOW_PROJ_DIR:-.}:/sources

* docker-compose-flower.yaml:
Flower service for monitoring workers (Celery workers)

x-airflow-common:
  &airflow-common
  image: my-com/airflow:2.9.3
  env_file: .env
  volumes:
    &volumes
    - /path/to/airflow/data/dags:/opt/airflow/dags
    - /path/to/airflow/data/logs:/opt/airflow/logs
    - /path/to/airflow/data/config:/opt/airflow/config
    - /path/to/airflow/data/plugins:/opt/airflow/plugins
  user: "${AIRFLOW_UID:-50000}:0"

services:
  flower:
    <<: *airflow-common
    command: celery flower
    ports:
      - "8080:5555"
    healthcheck:
      test: ["CMD", "curl", "--fail", "http://localhost:5555/"]
      interval: 30s
      timeout: 10s
      retries: 5
      start_period: 30s
    restart: always

# Launch
Make sure database instances are available and run:

docker compose -f docker-compose.yaml \
    -f docker-compose-init.yaml \
    -f docker-compose-worker.yaml \
    -f docker-compose-flower.yaml up -d

We can selectively deploy components on diferrent physical servers (e.g. deploy more workers):

docker compose -f docker-compose-flower.yaml up -d

Airflow should be running now!
Login with `admin/admin` at `http://localhost:8080`

# Dags
Start to deploy your dags to `/path/to/airflow/data/dags`

# Docker Best practices
* Pull an image:

docker pull postgres:15
docker pull quay.io/wildfly/wildfly

* Build an image:
`docker build -t my-app:latest -f Dockerfile .`
* Write a Dockerfile

FROM apache/airflow:2.9.3
RUN apt-get update && apt-get install -y git
USER airflow
RUN pip install airflow-dbt==0.4.0 dbt-core==1.7.1 dbt-redshift==1.7.0

* Run a container, stop, start:

docker run -d --name my-app ...
docker stop my-app
docker start my-app

* Mount a volume (e.g. shared data folder), map ports from host to container:

docker run -d --name my-app -p 8081:8080 -v /home/ubuntu/appdata/:/opt/myapp/data/ ...

* Pass env vars to containers:

docker run -d --name my-app -e MYVAR=myvalue \
        --env-file=/home/ubuntu/myapp.env my-app

* Restart policy (e.g. auto start containers on host restarts)

docker run -d --name my-app --restart unless-stopped my-app ... 

* Run with a specific user (usually set in the Dockerfile):

docker run -d --name my-app --user=airflow

* Limit resources for a container (e.g. for performance test):

docker run --cpus=0.5 --memory=1g my-app ...

* Exec into a running container(e.g. for debug):

docker exec -it my-app /bin/bash`

* List images, containers on host:

docker container list --all
docker image list --all

* Create a network and assign containers to it:

docker network create my_network

Containers on the same network can see each other by name:
docker run -d --name my-app --network my-network
docker run -d --name postgres_db --network my-network

* Monitoring processes, resources:

Show running containers: docker ps
Show resouces usage (mem, cpu) by containers: docker stats
Show running processes inside a container: docker top my-app